from grid_env_ideal_obs_repeat_task import *
from grid_agent import *
from checkpoint_utils import *
from maze_factory import *
from replay_config import *
import argparse
import json
import sys
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
from matplotlib.lines import Line2D
from sklearn.manifold import TSNE
import random
from sklearn.decomposition import PCA
from matplotlib.animation import FuncAnimation
from sklearn.cluster import KMeans
import threading
import mplcursors
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from scipy.spatial.distance import cdist

def progress_bar(current, total, barLength = 100):
    percent = float(current) * 100 / total
    arrow = '-' * int(percent/100 * barLength - 1) + '>'
    spaces = ' ' * (barLength - len(arrow))

    print('Progress: [%s%s] %d %%' % (arrow, spaces, percent), end='\r')
    sys.stdout.flush()

@partial(jax.jit, static_argnums=(3,))
def model_forward(variables, state, x, model):
    """ forward pass of the model
    """
    return model.apply(variables, state, x)

@jit
def get_action(y):
    return jnp.argmax(y)
get_action_vmap = jax.vmap(get_action)

# load landscape and states from file
def load_task(pth = "./logs/task.json", display = True):
    # open json file
    with open(pth, "r") as f:
        data = json.load(f)
        landscape = data["data"]
        state = data["state"]
        goal = data["goal"]
        if display:
            print("state: ", state)
            print("goal: ", goal)
            print("landscape: ", landscape)
    return landscape, state, goal


def main():

    seq_len = 15
    redundancy = 5
    diverse_set_capacity = 5

    """ parse arguments
    """
    rpl_config = ReplayConfig()

    parser = argparse.ArgumentParser()
    parser.add_argument("--model_pth", type=str, default=rpl_config.model_pth)
    parser.add_argument("--map_size", type=int, default=rpl_config.map_size)
    parser.add_argument("--task_pth", type=str, default=rpl_config.task_pth)
    parser.add_argument("--log_pth", type=str, default=rpl_config.log_pth)
    parser.add_argument("--nn_size", type=int, default=rpl_config.nn_size)
    parser.add_argument("--nn_type", type=str, default=rpl_config.nn_type)
    parser.add_argument("--show_kf", type=str, default=rpl_config.show_kf)
    parser.add_argument("--visualization", type=str, default=rpl_config.visualization)
    parser.add_argument("--video_output", type=str, default=rpl_config.video_output)
    parser.add_argument("--life_duration", type=int, default=rpl_config.life_duration)

    args = parser.parse_args()

    rpl_config.model_pth = args.model_pth
    rpl_config.map_size = args.map_size
    rpl_config.task_pth = args.task_pth
    rpl_config.log_pth = args.log_pth
    rpl_config.nn_size = args.nn_size
    rpl_config.nn_type = args.nn_type
    rpl_config.show_kf = args.show_kf
    rpl_config.visualization = args.visualization
    rpl_config.video_output = args.video_output
    rpl_config.life_duration = args.life_duration

    nn_type = ''
    if rpl_config.nn_type == "vanilla":
        nn_type = "vanilla"
    elif rpl_config.nn_type == "gru":
        nn_type = "gru"

    def load_data_and_compute(nn_type, seq_len, redundancy, diverse_set_capacity):

        rnn_limit_rings_file_name = "./logs/rnn_limit_rings_of_best_estimation_" + nn_type + "_" + str(seq_len) + "_" + str(redundancy) + "_" + str(diverse_set_capacity) + ".npz"
        # 载入 npz 文件
        rnn_limit_rings_of_best_estimation_file = np.load(rnn_limit_rings_file_name)

        # 获取 npz 文件中的所有对象名称
        matrix_names = rnn_limit_rings_of_best_estimation_file.files

        rnn_limit_rings_of_best_estimation = []

        # 遍历对象名称，访问和操作每个矩阵对象
        for name in matrix_names:
            matrix = rnn_limit_rings_of_best_estimation_file[name]
            rnn_limit_rings_of_best_estimation.append(matrix)

        # 求 rnn_limit_rings_of_best_estimation 的中心位置序列
        rnn_limit_rings_of_best_estimation_center = []
        for i in range(len(rnn_limit_rings_of_best_estimation)):
            rnn_limit_rings_of_best_estimation_center.append(np.mean(rnn_limit_rings_of_best_estimation[i], axis=(0,1)))
            # print("shape of rnn_limit_rings_of_best_estimation[i] is: ", rnn_limit_rings_of_best_estimation[i].shape)
        rnn_limit_rings_of_best_estimation_center = np.array(rnn_limit_rings_of_best_estimation_center)
        print("rnn_limit_rings_of_best_estimation_center.shape: ", rnn_limit_rings_of_best_estimation_center.shape)

        return rnn_limit_rings_of_best_estimation_center, copy.deepcopy(rnn_limit_rings_of_best_estimation)
    
    configs = [

        [nn_type, 6, 1, 100],
        [nn_type, 7, 1, 100],
        [nn_type, 8, 1, 100],
        [nn_type, 9, 1, 100],
        [nn_type, 10, 1, 100],
        [nn_type, 11, 1, 100],
        [nn_type, 12, 1, 100],
        [nn_type, 13, 1, 100],
        [nn_type, 14, 1, 100],
        [nn_type, 15, 1, 100],
        
        ]

    rnn_limit_rings_of_best_estimation_centers = []
    policy_rings_raw_data = []
    for i in range(len(configs)):
        centers, raw_data = load_data_and_compute(configs[i][0], configs[i][1], configs[i][2], configs[i][3])
        rnn_limit_rings_of_best_estimation_centers.append(centers)
        policy_rings_raw_data.append(raw_data)

    # 将 rnn_limit_rings_of_best_estimation_centers 所有元素拼接起来
    rnn_limit_rings_of_best_estimation_center_mat = np.concatenate(rnn_limit_rings_of_best_estimation_centers, axis=0)
    print("rnn_limit_rings_of_best_estimation_center_mat.shape: ", rnn_limit_rings_of_best_estimation_center_mat.shape)

    # 对 rnn_limit_rings_of_best_estimation_center_mat 进行 PCA
    pca = PCA()
    pca.fit(rnn_limit_rings_of_best_estimation_center_mat)

    # 使用 PCA 对 rnn_limit_rings_of_best_estimation_centers 中的所有数据，逐个进行降维
    rnn_limit_rings_of_best_estimation_centers_pca = []
    for i in range(len(rnn_limit_rings_of_best_estimation_centers)):
        rnn_limit_rings_of_best_estimation_centers_pca.append(pca.transform(rnn_limit_rings_of_best_estimation_centers[i]))

    # 载入神经轨迹文件
    file_name = "./logs/rnn_trajectories_qc_pass_" + rpl_config.nn_type + "_" + str(rpl_config.nn_size) + ".npz"
    rnn_trajectories_file = np.load(file_name)
    rnn_trajectories_qc_pass = rnn_trajectories_file["rnn_trajectories_qc_pass"]
    print("rnn_trajectories_qc_pass.shape: ", rnn_trajectories_qc_pass.shape)

    # jnp.corrcoef
    all_distances = []
    all_proximal_idx = []
    for i in range(rnn_trajectories_qc_pass.shape[0]):

        progress_bar(i, rnn_trajectories_qc_pass.shape[0])
        
        trj = rnn_trajectories_qc_pass[i]

        # Calculate the pairwise distances between trj and rnn_limit_rings_of_best_estimation_center_mat
        distances = cdist(trj, rnn_limit_rings_of_best_estimation_center_mat, metric='euclidean')

        # Find the minimum distance for each data point in trj
        min_distances = jnp.min(distances, axis=1)
        # Find the index of the minimum distance for each data point in trj
        min_distances_idx = jnp.argmin(distances, axis=1)

        # Convert the result to a 1D array
        result = min_distances.flatten()
        all_distances.append(result)

        result_idx = min_distances_idx.flatten()
        all_proximal_idx.append(result_idx)

    # 将所有 min_distances 使用折线图，绘制到同一个图中
    
    for i in range(len(all_distances)):
        progress_bar(i, len(all_distances))

        # 在 all_distances[i] 中，找到所有窗口大小为20的窗口内的最小值点
        min_distances_idx = []
        wind_sz = 100
        for j in range(len(all_distances[i])//wind_sz):
            progress_bar(j, len(all_distances[i])//wind_sz)
            min_distances_idx.append(np.argmin(all_distances[i][j*wind_sz:j*wind_sz+wind_sz]) + j*wind_sz)

        all_distances_min_distances = all_distances[i][np.array(min_distances_idx)]

        all_proximal_PR_idx = all_proximal_idx[i][np.array(min_distances_idx)]
        proximal_PR_points = rnn_limit_rings_of_best_estimation_center_mat[all_proximal_PR_idx]

        print("shape of proximal_ PR_points: ", proximal_PR_points.shape)

        # 计算 rnn_trajectories_qc_pass[i] 最后20个点的均值
        trj = rnn_trajectories_qc_pass[i]
        trj_last_20_mean = np.mean(trj[-20:], axis=0)

        # 寻找 rnn_limit_rings_of_best_estimation_center_mat 中距离 trj_last_20_mean 最近的点
        distances = []
        for j in range(rnn_limit_rings_of_best_estimation_center_mat.shape[0]):
            distances.append(np.linalg.norm(rnn_limit_rings_of_best_estimation_center_mat[j] - trj_last_20_mean))
        distances = np.array(distances)
        min_idx = np.argmin(distances)
        proximal_policy = rnn_limit_rings_of_best_estimation_center_mat[min_idx]

        # 计算 proximal_PR_points 中的每个点，与 trj_last_20_mean 的距离
        distances = []
        for j in range(proximal_PR_points.shape[0]):
            distances.append(np.linalg.norm(proximal_PR_points[j] - proximal_policy))

        fig = plt.figure()

        # Plot the first chart
        ax1 = fig.add_subplot(211)
        ax1.plot(distances, linewidth=1)

        # Plot the second chart
        ax2 = fig.add_subplot(212)
        ax2.plot(all_distances[i], linewidth=1)
        ax2.scatter(min_distances_idx, all_distances_min_distances, c='r', s=10)

        plt.show()



if __name__ == "__main__":
    main()